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Faculty of Economics


Jian, L., Linton, O. B., Tang, H., Zhang, Y.

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance


Abstract: We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probabilities of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.

Keywords: Covariate-adaptive randomization, High-dimensional data, Local average treatment effects, Randomized experiment, Regression adjustment

JEL Codes: C14 C21 I21

Author links: Oliver Linton  


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